A novel ensemble decision tree approach for mining genes coding ion channels for cardiopathy subtype

  • Authors:

  • Jie Zhang;Xia Li;Wei Jiang;Yanqiu Wang;Chuanxing Li;Qiuju Wang;Shaoqi Rao

  • Affiliations:

  • ,Department of Bioinformatics, Harbin Medical University, Harbin, P.R. China;Department of Bioinformatics, Harbin Medical University, Harbin, P.R. China;Department of Bioinformatics, Harbin Medical University, Harbin, P.R. China;Department of Bioinformatics, Harbin Medical University, Harbin, P.R. China;Department of Bioinformatics, Harbin Medical University, Harbin, P.R. China;Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, P.R. China;Department of Bioinformatics, Harbin Medical University, Harbin, P.R. China

  • Venue:
  • FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
  • Year:
  • 2005

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Abstract

Ion channels are critical for normal physiological function of humans and their functional abnormality may cause many disorders named channelopathy. Meanwhile, they are one of the few proteins that can be efficiently regulated by small molecule drugs, so they are ideal candidates for drug targets. Upon these viewpoints, it is known that research on ion channels will bring great scientific and practical value. Here, we applied a novel ensemble decision tree approach based on mining genes encoding the ion channels. Using this ensemble method, we analyzed an oligo array data set concerning the human cardiopathy which investigated by Medical College of Harvard University. By analyzing 57 samples and 1172 genes related to ion channels and other transmembrane proteins, we demonstrated that the ensemble approach can efficiently mine out disease related CACNA genes.